Identifying crosscutting concerns using historical code changes

  • Authors:

  • Bram Adams;Zhen Ming Jiang;Ahmed E. Hassan

  • Affiliations:

  • Queen's University, Canada;Queen's University, Canada;Queen's University, Canada

  • Venue:
  • Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
  • Year:
  • 2010

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Abstract

Detailed knowledge about implemented concerns in the source code is crucial for the cost-effective maintenance and successful evolution of large systems. Concern mining techniques can automatically suggest sets of related code fragments that likely contribute to the implementation of a concern. However, developers must then spend considerable time understanding and expanding these concern seeds to obtain the full concern implementation. We propose a new mining technique (COMMIT) that reduces this manual effort. COMMIT addresses three major shortcomings of current concern mining techniques: 1) their inability to merge seeds with small variations, 2) their tendency to ignore important facets of concerns, and 3) their lack of information about the relations between seeds. A comparative case study on two large open source C systems (Post-greSQL and NetBSD) shows that COMMIT recovers up to 87.5% more unique concerns than two leading concern mining techniques, and that the three techniques complement each other.